Dear Mohd
The (R-squared) , (also called the coefficient of determination), which is the proportion of variance (%) in the dependent variable that can be explained by the independent variable. Hence, as a rule of thumb for interpreting the strength of a relationship based on its R-squared value (use the absolute value of the R-squared value to make all values positive):
- if R-squared value < 0.3 this value is generally considered a None or Very weak effect size,
- if R-squared value 0.3 < r < 0.5 this value is generally considered a weak or low effect size,
- if R-squared value 0.5 < r < 0.7 this value is generally considered a Moderate effect size,
- if R-squared value r > 0.7 this value is generally considered strong effect size,
Ref: Source: Moore, D. S., Notz, W. I, & Flinger, M. A. (2013). The basic practice of statistics (6th ed.). New York, NY: W. H. Freeman and Company. Page (138).
also you can use other source:
Source: Zikmund, William G. (2000). Business research methods (6th ed). Fort Worth: Harcourt College Publishers. (Page 513)
I hope this information is helpful for you!
Kind regards!
Salah
Sometimes R squared value is around 0.3. is also considered valuable depends on research and research variables.
R Square at times can be misleading as it is affected by sample size and biases.
The R-squared (R2) value ranges from 0 to 1 with1 defines perfect predictive accuracy. Since R2 value is adopted in various research discipline, there is no standard guideline to determine the level of predictive acceptance. Henseler (2009) proposed a rule of thumb for acceptable R2 with 0.75, 0.50, and 0.25 are described as substantial, moderate and weak respectively.
( Henseler, J., Ringle, C., and Sinkovics, R. (2009). "The use of partial least squares path modeling in international marketing." Advances in International Marketing (AIM), 20, 277-320)
Your question is very nebulous at best. I don't think there is any hard rule concerning what is an acceptable R-squared value. There are number of factors involved, that may indicate that there is something else that is accounting for the total variance in the dependent variable that is explained by the independent variable, such as a mediating variable. However, Dr. Alhayri did explain a heuristic for acceptable R2 levels which are appropriate.
There is one of the PLS-SEM papers in IS research.
Article Structural equation modeling in information systems research...
Hello
I believe you should narrow down your focus on IS research. If you are studying on IS in healthcare domain, it would be better to look at the relevant papers to see the R sq values in the literature. Note that it is not only the value accepted/expected by the community (e.g. reviewers in the journals) but also the approximate value that researchers are able to explain in that field.
http://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit
I would say it depends on the purpose of the research and model. Not all research seeks to provide a comprehensive examination of variation in a dependent variable. One might be looking to explore more nuanced explanations of a dependent variable (eg moderating effects, change in explanatory power, mediating effects etc, rather than an overall model).
I think the answer would also depend on whether or not common method variance is an issue. If common method variance is an issue then a higher R^2 may just be an indication of that.
Check this out:
https://www.smartpls.com/resources/ebook_on_pls-sem.pdf
Does R square .042 to is acceptable ??? or all research will destroy?
Ambreen Waheed Minimum R square value suggested is 0.25 which is considered weak. Also, see past research for your respective model. What R square value is generally reported. This may give to an idea what R square should be there ideally.
How To Interpret R-squared in Regression Analysis:
https://statisticsbyjim.com/regression/interpret-r-squared-regression/